Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for providing persistent identifiers, comprising: accessing, by a first server, a first group of identifiers and a second group of identifiers, a first identifier from the first group of identifiers identifies a first cluster of devices having a first set of device identifications, a second identifier from the second group of identifiers identifies a second cluster of devices having a second set of device identifications; determining, by the first server, that the first cluster of devices and the second cluster of devices form an edge in a maximum cluster matching based at least in part on generating a plurality of pairs of identifiers from the first group of identifiers and the second group of identifiers; in response to determining that the first cluster of devices and the second cluster of devices form the edge in the maximum cluster matching, generating, by the first server, a persistent identifier based on the first identifier for a subset devices in the first cluster of devices and the second cluster of devices; and providing the persistent identifier, by the first server to a second server to interact with the subset devices based on the persistent identifier.
This invention relates to a system for managing device identifiers in a networked environment. The problem addressed is the need for persistent identifiers that can reliably track and interact with groups of devices, even as their individual device identifications change or as devices move between clusters. The solution involves clustering devices into groups and generating persistent identifiers that remain stable despite underlying changes in device identities. A server accesses two groups of identifiers. The first group identifies a first cluster of devices, each with a unique device identification, and the second group identifies a second cluster of devices, also with unique device identifications. The server determines whether the first and second clusters form an edge in a maximum cluster matching by generating pairs of identifiers from both groups. If they do, the server generates a persistent identifier for a subset of devices spanning both clusters. This persistent identifier is then provided to another server, which uses it to interact with the subset of devices. The approach ensures that devices can be reliably referenced even as their individual identifiers change or as they transition between clusters, maintaining consistency in device management and communication.
2. The method of claim 1 , further comprising: generating the first group of identifiers at a first time period; assigning the first identifier from the first group of identifiers to the first cluster of devices; generating the second group of identifiers at a second time period; and assigning the second identifier from the second group of identifiers to the second cluster of devices.
In the field of network management and device identification, a method addresses the challenge of dynamically assigning unique identifiers to clusters of devices over time. The method involves generating distinct groups of identifiers at different time periods and assigning these identifiers to specific clusters of devices. Initially, a first group of identifiers is generated during a first time period, and a first identifier from this group is assigned to a first cluster of devices. Subsequently, a second group of identifiers is generated during a second time period, and a second identifier from this group is assigned to a second cluster of devices. This approach ensures that identifiers are periodically refreshed and allocated to different device clusters, improving security and manageability in network environments. The method supports dynamic reconfiguration of device clusters and their associated identifiers, allowing for scalable and adaptable network operations. By separating identifier generation and assignment into distinct time periods, the system can efficiently track and manage device clusters while minimizing conflicts and ensuring unique identification across the network. This technique is particularly useful in large-scale networks where devices are frequently added, removed, or reorganized.
3. The method of claim 1 , wherein the generating the plurality of pairs of identifiers further comprises conducting an inner join operation on the first set of device identifications and the second set of device identification associated with the first group of identifiers and the second groups of identifiers respectively.
This invention relates to a method for generating pairs of device identifiers from two sets of device identifications to analyze relationships between devices. The method addresses the challenge of efficiently correlating devices across different datasets, which is critical for applications like network security, device tracking, and data integration. The method involves obtaining a first set of device identifications and a second set of device identifications, where each set is associated with a group of identifiers. The method then generates a plurality of pairs of identifiers by performing an inner join operation on the first set and the second set. The inner join operation ensures that only matching identifiers from both sets are paired, filtering out non-matching entries. This step is essential for accurately linking devices that appear in both datasets, enabling further analysis of their relationships. The method may also include additional steps such as filtering the generated pairs based on predefined criteria or analyzing the pairs to detect anomalies or patterns. The use of an inner join operation ensures that the pairing process is efficient and scalable, even when dealing with large datasets. This approach is particularly useful in scenarios where devices are identified differently in different systems, requiring a systematic way to correlate them. The invention improves the accuracy and reliability of device tracking and analysis in various technical domains.
4. The method of claim 3 , wherein the determining further comprises identifying, from the second group of identifiers, a first most frequent identifier that pairs most frequently with the first identifier in the plurality of pairs.
This invention relates to data analysis techniques for identifying relationships between identifiers in a dataset. The problem addressed is the need to efficiently and accurately determine the most significant relationships or associations between different identifiers within a dataset, particularly in scenarios where the data is unstructured or lacks explicit hierarchical relationships. The method involves analyzing a dataset containing pairs of identifiers to identify the most frequent co-occurrences. First, a plurality of pairs of identifiers is generated from the dataset, where each pair consists of two identifiers that appear together in the data. The pairs are then grouped into a first group associated with a first identifier and a second group associated with a second identifier. The method further includes determining the most frequent identifier in the second group that pairs most frequently with the first identifier. This involves analyzing the frequency of each identifier in the second group when paired with the first identifier and selecting the identifier with the highest frequency. The result is a prioritized relationship between the first identifier and the most frequently paired identifier from the second group, which can be used for further analysis, recommendation systems, or data clustering. The technique is particularly useful in applications such as social network analysis, recommendation engines, or any domain requiring the extraction of meaningful relationships from large datasets.
5. The method of claim 4 , wherein the determining further comprises discarding at least one selected pair from the plurality of pairs of identifiers, wherein the at least one selected pair includes the first identifier and another identifier that is not the first most frequent identifier from the second group of identifiers.
This invention relates to data processing techniques for identifying and filtering relevant pairs of identifiers from a dataset. The problem addressed is the efficient and accurate selection of meaningful identifier pairs while minimizing noise or irrelevant data. The method involves analyzing a plurality of pairs of identifiers, where each pair includes a first identifier and a second identifier. The identifiers are grouped into at least a first group and a second group based on their frequency or other criteria. The method determines the most frequent identifier in the second group and discards at least one selected pair that includes the first identifier paired with any identifier from the second group that is not the most frequent. This filtering step ensures that only the most relevant or significant pairs are retained, improving the accuracy of subsequent data analysis or processing tasks. The technique is particularly useful in applications such as data mining, pattern recognition, or network analysis, where identifying meaningful relationships between identifiers is critical. By discarding irrelevant pairs, the method reduces computational overhead and enhances the reliability of the results.
6. The method of claim 5 , wherein the determining further comprises identifying, from the first group of identifiers, a second most frequent identifier that pairs most frequently with the second identifier in the plurality of pairs.
Technical Summary: This invention relates to data analysis techniques for identifying relationships between identifiers in a dataset. The problem addressed is the need to efficiently determine the most significant associations between data elements, particularly in large datasets where direct analysis of all possible pairs is computationally expensive. The method involves analyzing a dataset containing pairs of identifiers to identify the most frequent associations. First, a first group of identifiers is selected based on their frequency of occurrence in the dataset. From this group, the most frequent identifier is identified, which pairs most frequently with a predefined second identifier. The method then further refines this analysis by identifying the second most frequent identifier from the first group that pairs most frequently with the second identifier. This step helps in uncovering secondary but still significant relationships that may not be immediately apparent from the most frequent associations alone. The technique is particularly useful in applications such as recommendation systems, network analysis, and pattern recognition, where understanding both primary and secondary relationships between data elements is crucial for accurate predictions or insights. By focusing on the most frequent pairs and their secondary associations, the method reduces computational complexity while still capturing meaningful relationships in the data.
7. The method of claim 6 , wherein the determining further comprises keeping only a selected pair for the second identifier that includes the second identifier and the second most frequent identifier from the first group of identifiers.
The invention relates to a method for processing identifiers in a data set to improve data analysis or machine learning tasks. The problem addressed is the need to efficiently reduce the number of identifiers while retaining meaningful relationships between them. This is particularly useful in applications like clustering, classification, or anomaly detection where excessive identifiers can degrade performance. The method involves analyzing a first group of identifiers to determine their frequency of occurrence. From this group, the most frequent identifier and the second most frequent identifier are selected. A second identifier is then compared to these selected identifiers. The method further includes keeping only a specific pair for the second identifier, which consists of the second identifier itself and the second most frequent identifier from the first group. This step ensures that the retained pair captures relevant relationships while reducing redundancy. The approach helps streamline data processing by focusing on the most significant identifiers, thereby improving computational efficiency and model accuracy. The method is applicable in various domains where identifier relationships are critical, such as network analysis, text mining, or bioinformatics. By selectively retaining pairs, the method balances data reduction with the preservation of important connections.
8. The method of claim 7 , wherein the determining further comprises identifying a pair including the second identifier from a remaining set of the plurality of pairs.
A system and method for processing data involves analyzing a dataset containing multiple pairs of identifiers to detect anomalies or specific patterns. The method includes receiving a dataset with a plurality of identifier pairs, where each pair consists of a first identifier and a second identifier. The method further involves determining a relationship or correlation between the identifiers in the pairs, which includes identifying a specific pair from the dataset that contains a predefined second identifier. This identification process is performed on a remaining subset of the plurality of pairs after an initial filtering or processing step. The method may also include additional steps such as filtering the dataset based on predefined criteria, comparing the identifiers to known patterns, or applying statistical analysis to detect anomalies. The goal is to efficiently and accurately identify relevant pairs of identifiers that meet specific conditions, which can be used for applications such as fraud detection, data validation, or pattern recognition in large datasets. The method ensures that the identification process is both precise and computationally efficient, even when dealing with large volumes of data.
9. The method of claim 8 , wherein the determining further comprises maximizing a sum of shared devices among the remaining set of the plurality of pairs in the maximum cluster matching.
This invention relates to optimizing device sharing in a networked system, particularly for maximizing the efficiency of resource allocation. The problem addressed is the need to efficiently pair and share devices across a network to minimize redundancy and improve resource utilization. The method involves analyzing a set of device pairs to identify the most optimal groupings for sharing, ensuring that the maximum number of devices are shared among the pairs in a given cluster. The process begins by evaluating a plurality of device pairs to determine which combinations yield the highest shared device count. This involves assessing the compatibility and availability of devices to form clusters where sharing is most beneficial. The method then refines the selection by removing pairs that do not contribute to the optimal sharing outcome, ensuring that the remaining pairs in the cluster maximize the sum of shared devices. This approach ensures that the network resources are used efficiently, reducing redundancy and improving overall system performance. The technique is particularly useful in environments where device sharing is critical, such as cloud computing, distributed systems, or shared infrastructure networks. By dynamically adjusting the pairings based on shared device metrics, the system can adapt to changing conditions and maintain optimal resource allocation.
10. An apparatus for providing persistent identifiers, comprising: a networking module to receive a first plurality of device identifications associated with a first cluster of devices at a first time period and a second plurality of device identifications associated with a second cluster of devices at a second time period; and a profile identification module, coupled to the networking module, to assign a first identifier from a first group of identifiers to identify the first cluster of devices, a second identifier from a second group of identifiers to identify the second cluster of devices; and to generate a persistent identifier for a subset devices in the first cluster of devices and the second cluster of devices in response to determining that the first cluster of devices and the second cluster of devices form an edge in a maximum cluster matching based at least in part on a plurality of pairs generated from the first group of identifiers and the second group of identifiers.
The apparatus is designed for managing device clusters in networked environments where devices may change over time, requiring persistent identification across different time periods. The problem addressed is the need to track and uniquely identify groups of devices that may dynamically form and reform, ensuring continuity in identification despite changes in device membership. The apparatus includes a networking module that receives device identifications from clusters of devices at different time periods. For example, it collects a first set of device identifications from a first cluster at one time and a second set from a second cluster at another time. A profile identification module processes these clusters, assigning unique identifiers from predefined groups to each cluster. If the system determines that the two clusters form an "edge" in a maximum cluster matching—a mathematical concept indicating a meaningful relationship between the clusters—it generates a persistent identifier for devices common to both clusters. This ensures that devices that appear in both clusters retain a consistent identifier, even as the overall cluster composition changes. The solution leverages clustering algorithms and identifier matching to maintain stable tracking of devices across dynamic network environments.
11. The apparatus of claim 10 , further comprising: a device identification module, coupled to the networking module, to assign the first identifier to identify the first cluster of devices at the first time period; assign the second identifier to identify the second cluster of devices at the second time period; and associate one or more device identifications from the second plurality of device identifications to respective ones from the first plurality of device identifications; and a data module, coupled to the device identification module and the profile identification module, to store or retrieve the first and second plurality of device identifications and the first and second group of identifiers.
This invention relates to a system for managing and tracking clusters of networked devices over time. The problem addressed is the need to dynamically identify and associate devices within a network as their groupings change, ensuring consistent tracking and data management across different time periods. The system includes a networking module that communicates with a plurality of devices, collecting device identifications from these devices. A device identification module assigns identifiers to clusters of devices at different time periods. For example, a first identifier is assigned to a first cluster of devices during a first time period, and a second identifier is assigned to a second cluster of devices during a second time period. The module also associates device identifications from the second time period with those from the first time period, allowing for tracking of devices as they move between clusters. A profile identification module generates profiles for the clusters, which may include characteristics or behaviors of the devices within each cluster. These profiles are linked to the assigned identifiers. A data module stores and retrieves the device identifications and cluster identifiers, ensuring that historical and current data can be accessed and analyzed. The system enables efficient tracking of device clusters over time, facilitating network management and analysis.
12. The apparatus of claim 11 , wherein the device identification module is further to generate the first group of identifiers at the first time period, and to generate the second group of identifiers at the second time period.
This invention relates to a system for managing device identifiers in a networked environment. The problem addressed is the need to dynamically generate and update device identifiers to enhance security, privacy, or operational efficiency in systems where devices interact with a central server or other networked components. The apparatus includes a device identification module that generates two distinct groups of identifiers for networked devices. The first group of identifiers is created during a first time period, and the second group is generated during a second time period. This temporal separation allows for periodic updates or rotations of identifiers, which can be useful for security purposes, such as preventing tracking or unauthorized access. The identifiers may be used to authenticate devices, manage access control, or facilitate communication between devices and a central server. The apparatus may also include a server configured to receive and process these identifiers, ensuring that devices are properly authenticated or authorized based on the identifiers they present. The system may further include a communication interface to transmit the identifiers between devices and the server, ensuring secure and reliable data exchange. The dynamic generation of identifiers at different time periods allows the system to adapt to changing security requirements or operational conditions, improving overall system resilience.
13. The apparatus of claim 10 , wherein the profile identification module is further to generate the plurality of pairs between the first group of identifiers and the second group of identifiers based on an inner join operation on device identifications associated with the first and second groups of identifiers.
This invention relates to a system for identifying and correlating device profiles in a network environment. The problem addressed is the difficulty in accurately matching and associating different identifiers from multiple groups, such as device identifiers from different networks or databases, to establish a comprehensive profile of a device or user. The apparatus includes a profile identification module that generates pairs between a first group of identifiers and a second group of identifiers. The module performs an inner join operation on device identifications associated with both groups to create these pairs. This operation ensures that only matching identifiers from both groups are paired, eliminating mismatches and improving accuracy. The system may also include a data collection module to gather identifiers from various sources and a storage module to maintain the generated pairs for further analysis or use. The inner join operation is a database technique that combines records from two tables based on a common field, in this case, device identifications. By applying this operation, the system efficiently correlates identifiers from different groups, enabling more reliable device profiling. This approach is particularly useful in scenarios where devices are identified differently across networks or systems, such as in cybersecurity, network management, or user behavior analysis. The system enhances the accuracy of device tracking and profiling by ensuring that only valid, matching pairs are generated.
14. The apparatus of claim 13 , wherein the profile identification module is further to identify, from the second group of identifiers, a first most frequent identifier that pairs with the first identifier in the plurality of pairs; and wherein the profile identification module is further to discard a first selected pair from the plurality of pairs when the first selected pair includes the first identifier and another identifier that is not the first most frequent identifier from the second group of identifiers.
This invention relates to a system for identifying and refining user profiles based on paired identifiers in a data set. The problem addressed is the challenge of accurately determining user profiles from noisy or incomplete data, where identifiers may be incorrectly paired or lack sufficient context to establish meaningful relationships. The system includes a profile identification module that processes a plurality of pairs of identifiers to refine profile associations. The module first identifies a first most frequent identifier from a second group of identifiers that pairs with a first identifier in the plurality of pairs. The module then discards a selected pair from the plurality of pairs if that pair includes the first identifier and another identifier that is not the most frequent identifier from the second group. This ensures that only the most relevant and statistically significant pairings are retained, improving the accuracy of profile identification. The system may also include a data processing module that generates the plurality of pairs from a data set, where each pair consists of two identifiers. The profile identification module further refines these pairs by filtering out those that do not meet predefined criteria, such as frequency thresholds or contextual relevance. This refinement process helps eliminate noise and inconsistencies in the data, leading to more reliable profile associations. The overall goal is to enhance the precision of user profiling by systematically validating and refining identifier pairings.
15. The apparatus of claim 14 , wherein the profile identification module is further to identify, from the first group of identifiers, a second most frequent identifier that pairs with the second identifier; and wherein the profile identification module is further to keep only a selected pair for the second identifier that includes the second identifier and the second most frequent identifier from the first group of identifiers.
This invention relates to a system for identifying and managing user profiles based on identifier relationships. The problem addressed is the challenge of accurately determining user profiles from fragmented or incomplete identifier data, such as usernames, email addresses, or device IDs, which may be scattered across multiple sources. The system includes a profile identification module that analyzes a first group of identifiers associated with a user. The module identifies the most frequent identifier in this group and pairs it with a second identifier to form a profile. To refine the profile, the module further identifies the second most frequent identifier in the first group and pairs it with the second identifier. Only the most relevant pair is retained for the second identifier, ensuring the profile is based on the strongest relationships between identifiers. The system may also include a data collection module to gather identifiers from various sources and a storage module to maintain the identified profiles. The profile identification module may use statistical or machine learning techniques to determine frequency and relevance, ensuring accurate profile construction even with noisy or incomplete data. This approach improves user profile accuracy in applications like fraud detection, personalized marketing, or user behavior analysis.
16. The apparatus of claim 15 , wherein the profile identification module is further to identify a pair including the first and second identifiers from a remaining set of the plurality of pairs, and to provide the first identifier as the persistent identifier.
This invention relates to a system for managing identifiers in a distributed computing environment, addressing the challenge of maintaining consistent and reliable identification across dynamic systems where identifiers may change over time. The apparatus includes a profile identification module that processes a plurality of identifier pairs, each pair consisting of a first identifier and a second identifier. The module analyzes these pairs to determine relationships between identifiers, particularly when one identifier is transient and another is persistent. The module identifies a pair from the remaining set of the plurality of pairs, where the first identifier is selected as the persistent identifier based on predefined criteria, such as stability, uniqueness, or consistency. This ensures that the system can reliably track entities even if their transient identifiers change. The apparatus may also include a storage module to retain the persistent identifier and a communication interface to interact with external systems, facilitating the propagation of the persistent identifier across the distributed environment. The invention improves system reliability by reducing ambiguity in entity identification, particularly in scenarios where identifiers are frequently updated or reassigned.
17. One or more non-transitory computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations, comprising: accessing a first group of identifiers and a second group of identifiers, a first identifier from the first group of identifiers to identify a first cluster of devices including a first set of device identifications, a second identifier from the second group of identifiers to identify a second cluster of devices including a second set of device identifications; determining that the first cluster of devices and the second cluster of devices form an edge in a maximum cluster matching based at least in part on a plurality of pairs generated from the first group of identifiers and the second group of identifiers; and in response to determining that the first cluster of devices and the second cluster of devices form the edge in the maximum cluster matching, generating a persistent identifier for a subset devices from the first cluster of devices and the second cluster of devices.
This invention relates to a system for managing and identifying clusters of devices in a networked environment. The problem addressed is the efficient grouping and tracking of devices across different clusters to enable persistent identification of subsets of devices, which is useful in scenarios like network management, device tracking, or resource allocation. The system involves accessing two groups of identifiers, where each group corresponds to a cluster of devices. The first group of identifiers is used to identify a first cluster of devices, which includes a set of device identifications. Similarly, the second group of identifiers is used to identify a second cluster of devices, which includes another set of device identifications. The system then determines whether the first and second clusters form an edge in a maximum cluster matching. This determination is based on analyzing pairs generated from the two groups of identifiers. If the clusters form such an edge, the system generates a persistent identifier for a subset of devices drawn from both the first and second clusters. This persistent identifier allows for consistent tracking of the subset across different clusters, improving device management and reducing redundancy. The approach leverages computational matching techniques to optimize the grouping and identification process.
18. The one or more non-transitory computer storage media of claim 17 , wherein the determining comprises: conducting an inner join operation on device identifications associated with the first group, of identifiers and the second groups of identifiers to generate the plurality of pairs.
This invention relates to a system for analyzing device identifiers to detect potential security threats or anomalies in a network. The problem addressed is the need to efficiently identify relationships between different groups of device identifiers to uncover hidden connections that may indicate malicious activity or unauthorized access. The solution involves a method for processing and comparing groups of identifiers to generate pairs that represent meaningful relationships. The system stores device identifiers in a database and organizes them into at least two distinct groups. These groups may represent different sources of data, such as network logs, authentication records, or device registrations. The system then performs an inner join operation on the identifiers from the first group and the second group to generate pairs of identifiers. This operation ensures that only identifiers present in both groups are matched, filtering out irrelevant or unrelated data. The resulting pairs are then analyzed to detect patterns, anomalies, or correlations that may indicate security risks, such as compromised devices or unauthorized access attempts. The system may further refine the analysis by applying additional filtering criteria or statistical methods to prioritize high-risk pairs for further investigation. The goal is to provide a scalable and efficient way to identify potential security threats by leveraging database operations to uncover hidden relationships between device identifiers.
19. The one or more non-transitory computer storage media of claim 18 , wherein the determining comprises: identifying, from the second group of identifiers, a first most frequent identifier that pairs most frequently with the first identifier in the plurality of pairs; discarding a first selected pair from the plurality of pairs wherein the first selected pair includes the first identifier and a third identifier that is not the first most frequent identifier from the second group of identifiers; identifying, from the first group of identifiers, a second most frequent identifier that pairs most frequently with the second identifier in the plurality of pairs; and keeping only a second selected pair for the second identifier that includes the second identifier and the second most frequent identifier from the first group of identifiers.
This invention relates to data processing techniques for analyzing and refining identifier pairs in a dataset. The problem addressed involves optimizing the selection of identifier pairs to improve data accuracy or relevance, particularly in scenarios where certain identifiers are more frequently paired together. The system processes a plurality of identifier pairs, where each pair consists of a first identifier from a first group and a second identifier from a second group. The method determines the most frequent pairing relationships between identifiers to refine the dataset. Specifically, it identifies the most frequent identifier from the second group that pairs with a given first identifier, then discards pairs that include the first identifier but not this most frequent identifier. Similarly, it identifies the most frequent identifier from the first group that pairs with a given second identifier and retains only the pair that includes the second identifier and this most frequent identifier. This process ensures that the retained pairs are the most statistically significant or relevant, improving data consistency and reducing noise. The technique is useful in applications such as data mining, recommendation systems, or network analysis where accurate pairing relationships are critical.
20. The one or more non-transitory computer storage media of claim 19 , wherein the determining further comprises identifying a pair including the first and second identifier from a remaining set of the plurality of pairs.
This invention relates to a system for analyzing data pairs to identify relationships between identifiers. The problem addressed is efficiently determining meaningful connections between data elements in large datasets, particularly when dealing with multiple potential pairings. The solution involves a method for processing a plurality of pairs of identifiers, where each pair includes a first identifier and a second identifier. The system determines a relationship between the identifiers by analyzing the pairs and identifying a specific pair from a remaining set of the plurality of pairs. This involves comparing the identifiers within the pairs to detect patterns, correlations, or other meaningful associations. The method may include filtering or ranking the pairs based on predefined criteria to isolate the most relevant connections. The system may also involve preprocessing the data to prepare the identifiers for analysis, such as normalizing or standardizing the identifiers to ensure consistency. The invention is particularly useful in applications like data matching, network analysis, or recommendation systems where identifying relationships between data elements is critical. The approach improves efficiency by focusing on a subset of pairs rather than analyzing all possible combinations, reducing computational overhead while maintaining accuracy.
Unknown
December 3, 2019
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